Fri Jun 29 08:59:09 CEST 2012WienSocial Network Analysis and Mining4321--340Development of computer science disciplines: a social network analysis approach12011analysis computer network paper scholary science scientometrics sna social In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published Journal Citation Report (JCR). Although this data cover most of the important journals, it lacks computer science conference and workshop proceedings, which results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences.Ranking scientific publications using a model of network traffichttp://puma.uni-kassel.de/bibtex/2ed618f45800255b5a5179d36849cd0b4/hothohotho2011-11-04T17:35:31+01:00network paper publication ranking scientific toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dylan Walker" itemprop="url" href="/author/Dylan%20Walker"><span itemprop="name">D. Walker</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Huafeng Xie" itemprop="url" href="/author/Huafeng%20Xie"><span itemprop="name">H. Xie</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Koon-Kiu Yan" itemprop="url" href="/author/Koon-Kiu%20Yan"><span itemprop="name">K. Yan</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sergei Maslov" itemprop="url" href="/author/Sergei%20Maslov"><span itemprop="name">S. Maslov</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Journal of Statistical Mechanics: Theory and Experiment</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">2007 </span></span>(<span itemprop="issueNumber">06</span>):
<span itemprop="pagination">P06010</span></em> </span>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)

Fri Nov 04 17:35:31 CET 2011Journal of Statistical Mechanics: Theory and Experiment06P06010Ranking scientific publications using a model of network traffic20072007network paper publication ranking scientific toread To account for strong ageing characteristics of citation networks, we modify the PageRank algorithm by initially distributing random surfers exponentially with age, in favour of more recent publications. The output of this algorithm, which we call CiteRank, is interpreted as approximate traffic to individual publications in a simple model of how researchers find new information. We optimize parameters of our algorithm to achieve the best performance. The results are compared for two rather different citation networks: all American Physical Society publications between 1893 and 2003 and the set of high-energy physics theory (hep-th) preprints. Despite major differences between these two networks, we find that their optimal parameters for the CiteRank algorithm are remarkably similar. The advantages and performance of CiteRank over more conventional methods of ranking publications are discussed.